"""
C++ 混合栈桥接模块
提供Python和C++之间的高性能数据交换
"""
from typing import List, Optional, Any
import ctypes
import numpy as np
try:
    from .data_model import TraceEvent
except ImportError:
    from data_model import TraceEvent


class CppEngineProxy:
    """C++引擎代理（占位实现）"""
    
    def __init__(self) -> None:
        self._dll: Optional[Any] = None
        self._initialized = False
        self._fallback_mode = True  # 回退到Python实现
    
    def initialize(self, dll_path: Optional[str] = None) -> bool:
        """初始化C++引擎"""
        try:
            if dll_path:
                # 尝试加载C++动态库
                # self._dll = ctypes.CDLL(dll_path)
                # self._setup_function_signatures()
                # self._initialized = True
                # self._fallback_mode = False
                pass
        except Exception as e:
            print(f"C++ engine initialization failed: {e}")
            print("Fallback to Python implementation")
            self._fallback_mode = True
        
        return self._initialized
    
    def process_events_cpp(self, events_array: np.ndarray) -> np.ndarray:
        """
        使用C++处理事件数据
        
        Args:
            events_array: numpy数组格式的事件数据
            
        Returns:
            处理后的numpy数组
        """
        if self._fallback_mode:
            return self._process_events_python(events_array)
        
        # C++实现占位
        # result = self._dll.process_events(...)
        return events_array
    
    def _process_events_python(self, events_array: np.ndarray) -> np.ndarray:
        """Python回退实现"""
        # 简单的数据处理示例
        return events_array
    
    def aggregate_metrics_cpp(self, data: np.ndarray) -> dict:
        """使用C++聚合指标"""
        if self._fallback_mode:
            return self._aggregate_metrics_python(data)
        
        # C++实现占位
        return {}
    
    def _aggregate_metrics_python(self, data: np.ndarray) -> dict:
        """Python回退实现"""
        if len(data) == 0:
            return {}
        
        return {
            'count': len(data),
            'mean': float(np.mean(data)),
            'std': float(np.std(data)),
            'min': float(np.min(data)),
            'max': float(np.max(data))
        }
    
    def batch_transform(self, events: List[TraceEvent]) -> np.ndarray:
        """批量转换事件为numpy数组（用于C++处理）"""
        if not events:
            return np.array([])
        
        # 转换为结构化数组
        dtype = np.dtype([
            ('timestamp', np.float64),
            ('thread_id', np.int32),
            ('process_id', np.int32),
            ('duration', np.float64)
        ])
        
        array = np.zeros(len(events), dtype=dtype)
        for i, event in enumerate(events):
            array[i] = (event.timestamp, event.thread_id, event.process_id, event.duration)
        
        return array


# 全局C++引擎实例
cpp_engine = CppEngineProxy()
